The present disclosure relates to a method for determining the working life of a traction battery of a boat, including the steps of determining a system configuration and/or operating conditions of the boat, providing a traction battery model, which states an ageing condition of the traction battery as time progresses depending on the system configuration and/or operating conditions determining at least one condition that confirms the end-of-life condition of the traction battery has been reached and calculating the time-period until the end-of-life condition is reached on the basis of the traction battery model.

The present disclosure relates to a method for determining the working life of a traction battery of a boat, including the steps of determining a system configuration and/or operating conditions of the boat, providing a traction battery model, which states an ageing condition of the traction battery as time progresses depending on the system configuration and/or operating conditions determining at least one condition that confirms the end-of-life condition of the traction battery has been reached and calculating the time-period until the end-of-life condition is reached on the basis of the traction battery model.

A device for determining the length of a vehicle combination comprises an input interface for receiving current driving dynamics data, in particular information regarding the current travel path of the towing vehicle, and a comparison unit for comparing the received current driving dynamics data with stored patterns of driving dynamics data that are typical for driving with a trailer of known dimensions, and an evaluation unit, which derives the length of the vehicle combination from the differences between the current driving dynamics data and the stored typical patterns of driving dynamics data. The device can use the sensors in the towing vehicle for obtaining the current driving dynamics data. Without additional hardware, a length of the vehicle combination, e.g. the length of a trailer connected to a towing vehicle, can be determined in this manner.

A device for determining the length of a vehicle combination comprises an input interface for receiving current driving dynamics data, in particular information regarding the current travel path of the towing vehicle, and a comparison unit for comparing the received current driving dynamics data with stored patterns of driving dynamics data that are typical for driving with a trailer of known dimensions, and an evaluation unit, which derives the length of the vehicle combination from the differences between the current driving dynamics data and the stored typical patterns of driving dynamics data. The device can use the sensors in the towing vehicle for obtaining the current driving dynamics data. Without additional hardware, a length of the vehicle combination, e.g. the length of a trailer connected to a towing vehicle, can be determined in this manner.

In one aspect, a method for a cloud-based computing system may include training, using test data, machine learning models to predict a remaining useful life of each cell of a battery pack of a vehicle. The method may include using a rule-based evaluator to determine first scores for the machine learning models, using a machine learning based metric evaluator to determine second scores for the machine learning models, using a model selection inference engine to select, based on the first and second scores for the machine learning models, a machine learning model to use to predict the remaining useful life of each cell of the battery pack of the vehicle, and transmitting, to a processing device of the vehicle, the selected machine learning model and parameters to predict the remaining useful life of each cell of the battery pack of the vehicle.

In one aspect, a method for a cloud-based computing system may include training, using test data, machine learning models to predict a remaining useful life of each cell of a battery pack of a vehicle. The method may include using a rule-based evaluator to determine first scores for the machine learning models, using a machine learning based metric evaluator to determine second scores for the machine learning models, using a model selection inference engine to select, based on the first and second scores for the machine learning models, a machine learning model to use to predict the remaining useful life of each cell of the battery pack of the vehicle, and transmitting, to a processing device of the vehicle, the selected machine learning model and parameters to predict the remaining useful life of each cell of the battery pack of the vehicle.

Embodiments of this application provide a data processing method and apparatus, and a device. In an example method, first data and a first historical driving feature are obtained, where the first data includes first driving data of a vehicle in a first time period, and the first historical driving feature is determined based on historical driving data of the vehicle. It is determined, based on the first data and the first historical driving feature, that the vehicle is abnormal in the first time period. A driving record video of the vehicle based on the first time period is annotated.

Embodiments of this application provide a data processing method and apparatus, and a device. In an example method, first data and a first historical driving feature are obtained, where the first data includes first driving data of a vehicle in a first time period, and the first historical driving feature is determined based on historical driving data of the vehicle. It is determined, based on the first data and the first historical driving feature, that the vehicle is abnormal in the first time period. A driving record video of the vehicle based on the first time period is annotated.

An information collection unit collects information indicating a driving operation of a vehicle by a driver and a state of the vehicle changed by the driving operation, and outputs, among the collected information, information in a period from a start point at which a driving operation contributing to stop of the vehicle is performed to a stop start point in a stop state where a stop time of the vehicle continues for a predetermined time or more, as driving operation information. A state estimation unit acquires driver state information indicating a state of the driver from a learned learner on which machine learning has been performed so as to output the driver state information when the driving operation information is input, by inputting the driving operation information output from the information collection unit to the learner and performing arithmetic processing of the learner.

An information collection unit collects information indicating a driving operation of a vehicle by a driver and a state of the vehicle changed by the driving operation, and outputs, among the collected information, information in a period from a start point at which a driving operation contributing to stop of the vehicle is performed to a stop start point in a stop state where a stop time of the vehicle continues for a predetermined time or more, as driving operation information. A state estimation unit acquires driver state information indicating a state of the driver from a learned learner on which machine learning has been performed so as to output the driver state information when the driving operation information is input, by inputting the driving operation information output from the information collection unit to the learner and performing arithmetic processing of the learner.